Sentiment_Classification_using_DistilBERT.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pranaya-mathur/Deep-Learning-Projects/blob/master/Sentiment_Classification_using_DistilBERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMZEiImFvbCy"
      },
      "source": [
        "# Sentiment Classification Using DistilBERT "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EE0wCLsGvhnM"
      },
      "source": [
        "We will use IMDB Movie Reviews Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2ZRiGpERSK1"
      },
      "source": [
        "# What is `DistilBERT`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yzge4wUw08Q"
      },
      "source": [
        "BERT is designed to pretrain deep bidirectional representations from\n",
        "unlabeled text by jointly conditioning on both\n",
        "left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer\n",
        "to create state-of-the-art models for a wide\n",
        "range of tasks, such as question answering and\n",
        "language inference, without substantial taskspecific architecture modifications."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMEy6Ptaw1Ab"
      },
      "source": [
        "DistilBERT is a small, fast, cheap and light Transformer model trained by distilling Bert base. It has 40% less parameters than bert-base-uncased, runs 60% faster while preserving over 95% of Bert’s performances as measured on the GLUE language understanding benchmark."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6X4HjQXtRY2c"
      },
      "source": [
        "![alt text](https://miro.medium.com/max/2000/1*IFVX74cEe8U5D1GveL1uZA.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4uCVI3wRYyx"
      },
      "source": [
        "## Why `DistilBERT`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bd5fc8HMRYtB"
      },
      "source": [
        "\n",
        "\n",
        "*   Accurate as much as Original BERT Model\n",
        "*   60% faster \n",
        "*   40% less parameters\n",
        "*   It can run on CPU\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_X4-4St4PN3Y"
      },
      "source": [
        "### Additional Reading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86WtEhL7os98"
      },
      "source": [
        "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter\n",
        "\n",
        "https://arxiv.org/abs/1910.01108"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6qkeIkR4w99"
      },
      "source": [
        "Video Lecture: BERT NLP Tutorial 1- Introduction | BERT Machine Learning | KGP Talkie\n",
        "\n",
        "https://www.youtube.com/watch?v=h_U27jBNYI4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CduweRwoRY6e"
      },
      "source": [
        "Ref BERT:  **Pre-training of Deep Bidirectional Transformers for\n",
        "Language Understanding**\n",
        "\n",
        "https://arxiv.org/abs/1810.04805"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_Od_6p6vj43"
      },
      "source": [
        "Understanding searches better than ever before:\n",
        "\n",
        "https://www.blog.google/products/search/search-language-understanding-bert/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHcY9C3HvkFv"
      },
      "source": [
        "Good Resource to Read More About the BERT: \n",
        "\n",
        "http://jalammar.github.io/illustrated-bert/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gk4fT8-_vjzI"
      },
      "source": [
        "Visual Guide to Using BERT:\n",
        " \n",
        "http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-BlBvZSTvqU"
      },
      "source": [
        "---------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lL9_qWcSRYnZ"
      },
      "source": [
        "# What is `ktrain`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-KPOCk1RYj2"
      },
      "source": [
        "ktrain is a library to help build, train, debug, and deploy neural networks in the deep learning software framework, Keras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSDYTd2jz_9x"
      },
      "source": [
        "ktrain uses tf.keras in TensorFlow instead of standalone Keras.) Inspired by the fastai library, with only a few lines of code, ktrain allows you to easily:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef5yYyUZz_4l"
      },
      "source": [
        "*   estimate an optimal learning rate for your model given your data using a learning rate finder\n",
        "*   employ learning rate schedules such as the triangular learning rate policy, 1cycle policy, and SGDR to more effectively train your model\n",
        "*   employ fast and easy-to-use pre-canned models for both text classification (e.g., NBSVM, fastText, GRU with pretrained word embeddings) and image classification (e.g., ResNet, Wide Residual Networks, Inception)\n",
        "*   load and preprocess text and image data from a variety of formats\n",
        "\n",
        "*   inspect data points that were misclassified to help improve your model\n",
        "*   leverage a simple prediction API for saving and deploying both models and data-preprocessing steps to make predictions on new raw data\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVzZNy0_z_0o"
      },
      "source": [
        "ktrain GitHub: https://github.com/amaiya/ktrain"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThRISEExRYgb"
      },
      "source": [
        "# Notebook Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhnjIqyT4adZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56a02fc8-ed82-490a-a572-464869de0848"
      },
      "source": [
        "!pip install ktrain"
      ],
      "execution_count": 1,
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            "Collecting langdetect\n",
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            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from syntok->ktrain) (2019.12.20)\n",
            "Requirement already satisfied: Keras>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from seqeval==0.0.19->ktrain) (2.4.3)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 51.7MB/s \n",
            "\u001b[?25hCollecting tokenizers==0.9.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/34/b39eb9994bc3c999270b69c9eea40ecc6f0e97991dba28282b9fd32d44ee/tokenizers-0.9.3-cp36-cp36m-manylinux1_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 50.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers<4.0,>=3.1.0->ktrain) (0.8)\n",
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            "Requirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from transformers<4.0,>=3.1.0->ktrain) (3.12.4)\n",
            "Collecting keras-transformer>=0.38.0\n",
            "  Downloading https://files.pythonhosted.org/packages/89/6c/d6f0c164f4cc16fbc0d0fea85f5526e87a7d2df7b077809e422a7e626150/keras-transformer-0.38.0.tar.gz\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from Keras>=2.2.4->seqeval==0.0.19->ktrain) (3.13)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers<4.0,>=3.1.0->ktrain) (7.1.2)\n",
            "Collecting keras-pos-embd>=0.11.0\n",
            "  Downloading https://files.pythonhosted.org/packages/09/70/b63ed8fc660da2bb6ae29b9895401c628da5740c048c190b5d7107cadd02/keras-pos-embd-0.11.0.tar.gz\n",
            "Collecting keras-multi-head>=0.27.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e6/32/45adf2549450aca7867deccfa04af80a0ab1ca139af44b16bc669e0e09cd/keras-multi-head-0.27.0.tar.gz\n",
            "Collecting keras-layer-normalization>=0.14.0\n",
            "  Downloading https://files.pythonhosted.org/packages/a4/0e/d1078df0494bac9ce1a67954e5380b6e7569668f0f3b50a9531c62c1fc4a/keras-layer-normalization-0.14.0.tar.gz\n",
            "Collecting keras-position-wise-feed-forward>=0.6.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e3/59/f0faa1037c033059e7e9e7758e6c23b4d1c0772cd48de14c4b6fd4033ad5/keras-position-wise-feed-forward-0.6.0.tar.gz\n",
            "Collecting keras-embed-sim>=0.8.0\n",
            "  Downloading https://files.pythonhosted.org/packages/57/ef/61a1e39082c9e1834a2d09261d4a0b69f7c818b359216d4e1912b20b1c86/keras-embed-sim-0.8.0.tar.gz\n",
            "Collecting keras-self-attention==0.46.0\n",
            "  Downloading https://files.pythonhosted.org/packages/15/6b/c804924a056955fa1f3ff767945187103cfc851ba9bd0fc5a6c6bc18e2eb/keras-self-attention-0.46.0.tar.gz\n",
            "Building wheels for collected packages: ktrain, langdetect, syntok, seqeval, keras-bert, sacremoses, keras-transformer, keras-pos-embd, keras-multi-head, keras-layer-normalization, keras-position-wise-feed-forward, keras-embed-sim, keras-self-attention\n",
            "  Building wheel for ktrain (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Created wheel for langdetect: filename=langdetect-1.0.8-cp36-none-any.whl size=993195 sha256=8134761c440918682733becbb7e8418183d05cc153d505deb70d9accc19b5913\n",
            "  Stored in directory: /root/.cache/pip/wheels/8d/b3/aa/6d99de9f3841d7d3d40a60ea06e6d669e8e5012e6c8b947a57\n",
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            "  Created wheel for syntok: filename=syntok-1.3.1-cp36-none-any.whl size=20919 sha256=6ec84681dc4458180a850105c373788b1923a112218004ad3b0e090b210a17d1\n",
            "  Stored in directory: /root/.cache/pip/wheels/51/c6/a4/be1920586c49469846bcd2888200bdecfe109ec421dab9be2d\n",
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            "  Created wheel for keras-bert: filename=keras_bert-0.86.0-cp36-none-any.whl size=34145 sha256=a289e59c9d396c4e3ad37ca4254857bbb255344b42aa2f48f85695f8f29bc5e8\n",
            "  Stored in directory: /root/.cache/pip/wheels/66/f0/b1/748128b58562fc9e31b907bb5e2ab6a35eb37695e83911236b\n",
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            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=193ad4bafa1808faa5780fab7055d1cdea7b69c60ab8a4e7df74491677f82f5b\n",
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            "  Created wheel for keras-transformer: filename=keras_transformer-0.38.0-cp36-none-any.whl size=12942 sha256=2ec65de36f5bcc1dbb4a615dec7e94f8e02d2ed2d2d2736816e2c5764a809f04\n",
            "  Stored in directory: /root/.cache/pip/wheels/e5/fb/3a/37b2b9326c799aa010ae46a04ddb04f320d8c77c0b7e837f4e\n",
            "  Building wheel for keras-pos-embd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-pos-embd: filename=keras_pos_embd-0.11.0-cp36-none-any.whl size=7554 sha256=334bdc4faa8b00de5dd8220974ca0294f9ccaa44e9c84ffb6faee6c8e9641350\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/a1/a0/ce6b1d49ba1a9a76f592e70cf297b05c96bc9f418146761032\n",
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            "  Created wheel for keras-multi-head: filename=keras_multi_head-0.27.0-cp36-none-any.whl size=15612 sha256=749b5298645e1f673e422fe7d40d94c99249f5dfe7c7eaf10efd1b59bd836d45\n",
            "  Stored in directory: /root/.cache/pip/wheels/b5/b4/49/0a0c27dcb93c13af02fea254ff51d1a43a924dd4e5b7a7164d\n",
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            "  Created wheel for keras-layer-normalization: filename=keras_layer_normalization-0.14.0-cp36-none-any.whl size=5268 sha256=6c6e3370bd1e9b04adb4b1c40d586e44423cb685ce1547e2a73a0120cedf4f0f\n",
            "  Stored in directory: /root/.cache/pip/wheels/54/80/22/a638a7d406fd155e507aa33d703e3fa2612b9eb7bb4f4fe667\n",
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            "  Created wheel for keras-position-wise-feed-forward: filename=keras_position_wise_feed_forward-0.6.0-cp36-none-any.whl size=5626 sha256=6fe43e48806e58ff3d75d21dc73a0d107bcdb743f56cbf2039362b5d8d1ac07d\n",
            "  Stored in directory: /root/.cache/pip/wheels/39/e2/e2/3514fef126a00574b13bc0b9e23891800158df3a3c19c96e3b\n",
            "  Building wheel for keras-embed-sim (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-embed-sim: filename=keras_embed_sim-0.8.0-cp36-none-any.whl size=4559 sha256=4262507a89e5fd03e4f4ee56baca6588f94b9bd30461a90ef2bb0ea44ceec7eb\n",
            "  Stored in directory: /root/.cache/pip/wheels/49/45/8b/c111f6cc8bec253e984677de73a6f4f5d2f1649f42aac191c8\n",
            "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-self-attention: filename=keras_self_attention-0.46.0-cp36-none-any.whl size=17278 sha256=b9ce2b69941480494aeaac428b1f525d9a8c9bb5f3b9e5a71fc6e1395e98396d\n",
            "  Stored in directory: /root/.cache/pip/wheels/d2/2e/80/fec4c05eb23c8e13b790e26d207d6e0ffe8013fad8c6bdd4d2\n",
            "Successfully built ktrain langdetect syntok seqeval keras-bert sacremoses keras-transformer keras-pos-embd keras-multi-head keras-layer-normalization keras-position-wise-feed-forward keras-embed-sim keras-self-attention\n",
            "\u001b[31mERROR: transformers 3.5.1 has requirement sentencepiece==0.1.91, but you'll have sentencepiece 0.1.94 which is incompatible.\u001b[0m\n",
            "Installing collected packages: langdetect, cchardet, syntok, seqeval, sacremoses, tokenizers, sentencepiece, transformers, keras-pos-embd, keras-self-attention, keras-multi-head, keras-layer-normalization, keras-position-wise-feed-forward, keras-embed-sim, keras-transformer, keras-bert, whoosh, ktrain\n",
            "Successfully installed cchardet-2.1.7 keras-bert-0.86.0 keras-embed-sim-0.8.0 keras-layer-normalization-0.14.0 keras-multi-head-0.27.0 keras-pos-embd-0.11.0 keras-position-wise-feed-forward-0.6.0 keras-self-attention-0.46.0 keras-transformer-0.38.0 ktrain-0.25.2 langdetect-1.0.8 sacremoses-0.0.43 sentencepiece-0.1.94 seqeval-0.0.19 syntok-1.3.1 tokenizers-0.9.3 transformers-3.5.1 whoosh-2.7.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8lwTCvX4abU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1681e1b-32be-4e63-9e78-4990946350de"
      },
      "source": [
        "!git clone https://github.com/laxmimerit/IMDB-Movie-Reviews-Large-Dataset-50k.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'IMDB-Movie-Reviews-Large-Dataset-50k'...\n",
            "remote: Enumerating objects: 10, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 10 (delta 1), reused 0 (delta 0), pack-reused 0\n",
            "Unpacking objects: 100% (10/10), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GepWKkM4aVF"
      },
      "source": [
        "# /content/IMDB-Movie-Reviews-Large-Dataset-50k"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YW_Jxjqf4aSJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ktrain\n",
        "from ktrain import text\n",
        "import tensorflow as tf"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYCOq0GB0edr"
      },
      "source": [
        "data_test = pd.read_excel('/content/IMDB-Movie-Reviews-Large-Dataset-50k/test.xlsx', dtype= str)\n",
        "data_train = pd.read_excel('/content/IMDB-Movie-Reviews-Large-Dataset-50k/train.xlsx', dtype = str)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPffRgyM1Vc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e14fcf11-e09c-4f0f-a5dd-670ac51e60b9"
      },
      "source": [
        "data_train.sample(5)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14236</th>\n",
              "      <td>You, know, I can take the blood and the sex, b...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16222</th>\n",
              "      <td>A film without conscience. Drifter agrees to k...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10585</th>\n",
              "      <td>Oh if only I could give this rubbish less than...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10692</th>\n",
              "      <td>What an awful movie. I love monster flicks but...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17972</th>\n",
              "      <td>I like Chris Rock, but I feel he is wasted in ...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 Reviews Sentiment\n",
              "14236  You, know, I can take the blood and the sex, b...       neg\n",
              "16222  A film without conscience. Drifter agrees to k...       neg\n",
              "10585  Oh if only I could give this rubbish less than...       neg\n",
              "10692  What an awful movie. I love monster flicks but...       neg\n",
              "17972  I like Chris Rock, but I feel he is wasted in ...       neg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnXGDGYE1Yn5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69371bf0-031d-4682-9bdb-abc1e5f90944"
      },
      "source": [
        "text.print_text_classifiers()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fasttext: a fastText-like model [http://arxiv.org/pdf/1607.01759.pdf]\n",
            "logreg: logistic regression using a trainable Embedding layer\n",
            "nbsvm: NBSVM model [http://www.aclweb.org/anthology/P12-2018]\n",
            "bigru: Bidirectional GRU with pretrained fasttext word vectors [https://fasttext.cc/docs/en/crawl-vectors.html]\n",
            "standard_gru: simple 2-layer GRU with randomly initialized embeddings\n",
            "bert: Bidirectional Encoder Representations from Transformers (BERT) from keras_bert [https://arxiv.org/abs/1810.04805]\n",
            "distilbert: distilled, smaller, and faster BERT from Hugging Face transformers [https://arxiv.org/abs/1910.01108]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArjDvkJn1pwp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407,
          "referenced_widgets": [
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            "061b6df6a3bf4374915ebf109d80cd69",
            "9054d33a0ed0450db3ef9afbda31de11",
            "8edf4f4c8d4c4d0d85acd2644b2c165a",
            "32674fb5b7734ee988d9c7fd8df748cf",
            "347b9ca582cd4010b4f0266cd391298d",
            "b0bbc828a3984286b57f684660e51172",
            "b680b07a43b443959607283f473d0353",
            "3bd568267b6248b0adf5a38cf98d1b60"
          ]
        },
        "outputId": "754119df-2bb4-4c67-ab98-8b62bb56d4af"
      },
      "source": [
        "(train, val, preproc) = text.texts_from_df(train_df=data_train, text_column='Reviews', label_columns='Sentiment',\n",
        "                   val_df = data_test,\n",
        "                   maxlen = 400,\n",
        "                   preprocess_mode = 'distilbert')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c720a842818f4ffab646752c75dc7062",
              "version_minor": 0,
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 234\n",
            "\t95percentile : 598\n",
            "\t99percentile : 913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f74f86b26a8a4ff39bfa27bc4b976b87",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "061b6df6a3bf4374915ebf109d80cd69",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: en\n",
            "test sequence lengths:\n",
            "\tmean : 234\n",
            "\t95percentile : 598\n",
            "\t99percentile : 913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnwYNxJb2XIm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122,
          "referenced_widgets": [
            "54310250c3f947fb8073a13624b6eb7a",
            "d6aa0078c51f48bd9149d69fa69b6459",
            "63ebe9d1d75a4ee59ed6cbb49474b7d3",
            "e238cfd44f964222888a2bc8cd6fc9b8",
            "6eeaca8f01034583b25075295dea5251",
            "79e279ad6c164d98951443d22956c2be",
            "c5adee5c2aa6496984a4d8583fc3e16e",
            "953463e5991f49318361e010c4ac74e3"
          ]
        },
        "outputId": "499a59ae-de9e-432c-b376-edd2c6091506"
      },
      "source": [
        "model = text.text_classifier(name = 'distilbert', train_data = train, preproc=preproc)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "maxlen is 400\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54310250c3f947fb8073a13624b6eb7a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=363423424.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26QFg7VV4nvG"
      },
      "source": [
        "learner = ktrain.get_learner(model = model,\n",
        "                             train_data = train,\n",
        "                             val_data = val,\n",
        "                             batch_size = 6)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNcVJ__v4nqE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dccc136-bd7e-441e-f5cb-0ae6a77cd010"
      },
      "source": [
        "learner.fit_onecycle(lr = 2e-5, epochs=2)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 2e-05...\n",
            "Epoch 1/2\n",
            "4167/4167 [==============================] - 1646s 395ms/step - loss: 0.2969 - accuracy: 0.8733 - val_loss: 0.1601 - val_accuracy: 0.9430\n",
            "Epoch 2/2\n",
            "4167/4167 [==============================] - 1654s 397ms/step - loss: 0.1553 - accuracy: 0.9419 - val_loss: 0.0619 - val_accuracy: 0.9824\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f944fbfe5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rW8bsakl4nl6"
      },
      "source": [
        "predictor = ktrain.get_predictor(learner.model, preproc)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NLK_GuB4nim"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSfKspeDVfI8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "4c897140-bd5f-4162-e02c-5d43dcffb744"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xI0NpQN4nf4"
      },
      "source": [
        "predictor.save('/content/drive/My Drive/distilbert')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnqXw9CA4ndM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBN4Jk8u4naa"
      },
      "source": [
        "data = ['this movie was really bad. acting was also bad. I will not watch again',\n",
        "        'the movie was really great. I will see it again', 'another great movie. must watch to everyone']"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkJWZAXk4nXM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41620809-e6ad-491f-bab4-22461fe241f5"
      },
      "source": [
        "predictor.predict(data)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['neg', 'pos', 'pos']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oiO-7qy26Zp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0d071b0-d470-4c56-bfe6-3d5af3f00070"
      },
      "source": [
        "predictor.get_classes()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['neg', 'pos']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl8bGAUoW-A7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2acd4043-3fc3-4839-c807-ce5f4a7b6604"
      },
      "source": [
        "predictor.predict(data, return_proba=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.994932  , 0.00506797],\n",
              "       [0.0043839 , 0.9956161 ],\n",
              "       [0.00323881, 0.99676126]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34y6Ud0QXL0i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}